OP0269 DEVELOPMENT AND EVALUATION OF MACHINE LEARNING ALGORITHMS FOR THE PREDICTION OF OPIOID-RELATED DEATHS AMONG UK PATIENTS WITH NON-CANCER PAIN
Authors: J. Benitez-Aurioles, D. Jenkins, C. Ramirez Medina, N. Peek, M. Jani
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> There has been a sharp rise in opioid use for non-cancer pain globally, including for rheumatic and musculoskeletal conditions. Despite increased awareness of adverse effects, they remain commonly prescribed in most countries. Clinical prediction models (CPMs) offer the possibility of assessing individual risk allowing better allocation of resources towards those at risk. Machine learning (ML) approaches can address nonlinear relationships and complex interactions between variables and are increasingly used to develop CPMs. <h3>Objectives</h3> To develop, validate, and compare the performance of three CPMs based on regression and ML, which leverage primary care data to estimate the risk of opioid-related death in patients prescribed opioids for non-cancer pain. <h3>Methods</h3> Patients ≥18 years old without prior cancer who were prescribed any opioid between 01/01/2006 and 31/12/2017 were identified in the Clinical Practice Research Datalink (CPRD), representative of national patient data from UK primary care. Only new opioid users were included. Index date was date of first prescription, with censoring at withdrawal from the CPRD or after not having an opioid prescription for two years. Baseline data were extracted from each patient's records, including demographic information, comorbidities, concomitant medications, and the opioid type being prescribed. 49 candidate predictors were used to train three competing risk models: a Fine&amp;Gray regression model with LASSO regularisation, a survival random forest (RF), and a neural network (DeepHit). The outcome was opioid-related mortality and other cause mortality the competing event, defined using a curated ICD-10 codelist. Predictive performance of the models, like area under the receiver characteristic operator curve (AUCROC) were calculated using 5-fold cross validation. <h3>Results</h3> We included a total of 1,029,681 patients, of which 1,240 experienced an opioid-related death, and 52,833 experienced a competing death. The Fine&amp;Gray, RF and DeepHit models achieved average AUCROC values of 0.83(95% CI: 0.81-0.85), 0.78(0.77-0.79) and 0.81(0.80-0.82) respectively (Figure 1). At optimum risk cut point, as per Youden's index, the models achieved sensitivities of 0.82(0.78-0.85), 0.75(0.67-0.82) and 0.80(0.78-0.83), and specificities of 0.78(0.73-0.82), 0.75(0.68-0.83) and 0.78(0.75-0.8) when predicting 12-month risk, respectively. In the Fine&amp;Gray model, factors associated with increased risk were history of substance use disorder (hazards ratio [HR]: 3.40, 95% CI:3.12-3.69) and alcohol abuse (HR:3.07, 95% CI:2.93-3.22). COPD (HR:1.53, 95% CI:1.48-1.58) and moderate liver disease (HR:1.31, 95% CI:0.99-1.63) were the comorbidities associated with highest risk. Morphine (HR:2.39, 95% CI:2.08-2.69) and oxycodone (HR:1.10, 95% CI:1.00-1.20) at initiation and concomitant gabapentinoids (HR:1.99, 95% CI:1.80-2.18) and benzodiazepines (HR:1.30, 95% CI:1.24-1.36) were associated with an increased risk. HR for rheumatologic diseases was 1.08 (95% CI:1.01-1.14). <h3>Conclusion</h3> The Fine&amp;Gray and DeepHit models showed comparable discriminative performance. Substance abuse, lung and liver comorbidities, morphine or oxycodone at initiation and co-prescription of gabapentinoids and benzodiazepine, were some of the factors associated with a higher risk of opioid-related mortality. <h3>Acknowledgements</h3> JBA acknowledges the receipt of studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome PhD Programme in Health Data Science (Grant Ref: 218529/Z/19/Z). MJ is funded through an NIHR Advanced Fellowship (NIHR301413). <h3>Disclosure of Interests</h3> None Declared.